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The UCAM-SENS unit is seeking a highly motivated PhD student to join its cutting-edge research in electrochemical devices. This position focuses on the development of next-generation electrochemical
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to help build the Quantum Industry through a Pan-European mobility program. A number of fellowships therefore exist to support outstanding DigiQ students to undertake a research stay (internship or master’s
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such as ATAC-seq, chIP-seq and 3D chromatin conformation capture data You have experience in the analysis of single-cell data Education and training You hold a PhD degree in Computational Biology Languages
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Implants Team Mechanical engineering, medicine, biology, information science, instrumentation 16 Microbiome & Oral/Systemic Diseases Mechanisms Team Dentistry, microbiology, immunology, biochemistry, cell
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-space models and advanced attention mechanisms to new memory paradigms—to enable models to understand and synthesize information from millions of tokens, entire codebases, or extensive scientific
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to prevent and treat decompensated cirrhosis and acute-on-chronic liver failure (ACLF) and reduce mortality by investigating the human gut microbiome. The goal is to identify predictors and mechanisms
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Research Engineer - Tools developer for LSQUANT platform (Theoretical and Computational Nanoscience)
different areas of nanoscience and nanotechnology. Job Title: Research Engineer - Tools developer for LSQUANT platform Research area or group: Theoretical and Computational Nanoscience Group Description
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discovery, and the development of novel diagnostic and therapeutic devices. With the goal of “AI for Science,” we are recruiting highly motivated PhD-level researchers with backgrounds in computer
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of the tribometer include the following tasks: Design automated load selection synchronized with mechanical stimulus. Design current and voltage sensing for power generation characterization. Model time response
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of carbon materials, low-dimensional materials and others Multi-scale computation method development Data-driven experiments AI for materials science Multi-scale modeling for materials manufacturing mechanism